An algorithm to evaluate the number of trabecular cell layers using nucleus arrangement applied to hepatocellular carcinoma
An algorithm to evaluate the number of trabecular cell layers using nucleus arrangement applied to hepatocellular carcinoma
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一种利用细胞核排列评估小梁细胞层数的算法应用于肝细胞癌
DOI：
10.1117/12.2006319
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
M. Sakamoto
中科院分区：
文献类型：
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作者：
Hideki Komagata;Naoki Kobayashi;A. Katoh;Y. Ohnuki;M. Ishikawa;Kazuma Shinoda;Masahiro Yamaguchi;T. Abe;A. Hashiguchi;M. Sakamoto
信息技术的最新进展改善了病理学虚拟切片技术和病理学图像的诊断支持系统研究。诊断支持系统利用由图像处理确定的定量指标。在以前的研究中，诊断支持系统，乳腺癌或肺癌的区域已被识别的核的大小，复杂性，核间距离的基础上，在其他功能的特征量。提高识别精度对于添加新的特征量是重要的。我们专注于肝细胞癌（HCC），并探讨新的特征量的组织学图像的HCC。肝癌最重要的组织学特征之一是小梁模式。对于癌症的诊断，识别肿瘤细胞小梁是重要的。我们提出了一种新的算法来计算细胞层的数目在组织切片染色的肝细胞癌的组织学图像。对于计算，我们使用基于细胞核中点的Delaunay图，从Delaunay图中删除正弦和脂肪滴区域，并在应用细化算法的同时对Delaunay线进行计数。此外，我们实验了不同组织学分级的HCC的细胞层数的计算与我们的方法。细胞层数区分肿瘤分化和Edmondson等级，因此，我们的算法可以作为诊断支持系统的HCC的指标。
Recent advances in information technology have improved pathological virtual-slide technology and diagnostic support system studies of pathological images. Diagnostic support systems utilize quantitative indices determined by image processing. In previous studies on diagnostic support systems, carcinomatous areas of breast or lung have been recognized by the feature quantities of nuclear sizes, complexities, and internuclear distances based on graph theory, among other features. Improving recognition accuracy is important for the addition of new feature quantities. We focused on hepatocellular carcinoma (HCC) and investigated new feature quantities of histological images of HCC. One of the most important histological features of HCC is the trabecular pattern. For diagnosing cancer, it is important to recognize the tumor cell trabeculae. We propose a new algorithm for calculating the number of cell layers in histological images of HCC in tissue sections stained by hematoxylin and eosin. For the calculation, we used a Delaunay diagram that was based on the median points of nuclei, deleted the sinusoid and fat droplet regions from the Delaunay diagram, and counted the Delaunay lines while applying a thinning algorithm. Moreover, we experimented with the calculation of the number of cell layers with our method for different histological grades of HCC. The number of cell layers discriminated tumor differentiations and Edmondson grades; therefore, our algorithm may serve as an index of HCC for diagnostic support systems.